GitLab

* Add run_eagerly for ctl.

* fix test name and do not set "default".

* force_v2_in_keras_compile FLAG default to None and added seperate temp path.

* switch to force testing 1v path not force v2 path.

* Rename function force_v1_path.

* Merged commit includes the following changes:
260800447  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix tf function signature errors for NCF model.

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260767064  by psv<psv@google.com>:

    Renames the `run_distributed` flag to `experimental_run_tf_function` and modifies the tests generated by the keras_all_modes decorator to better reflect that.

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PiperOrigin-RevId: 260800447

* Update ncf_keras_benchmark.py

* Update ncf_keras_benchmark.py

* Update ncf_keras_benchmark.py

* Update ncf_keras_benchmark.py

260622966  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add absl_app import.

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PiperOrigin-RevId: 260622966

260228553  by priyag<priyag@google.com>:

    Enable transformer and NCF official model tests. Also fix some minor issues so that all tests pass with TF 1 + enable_v2_behavior.

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260043210  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add logic to train NCF model using offline generated data.

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259778607  by priyag<priyag@google.com>:

    Internal change

259656389  by hongkuny<hongkuny@google.com>:

    Internal change

PiperOrigin-RevId: 260228553

* Add force_run_distributed tests.

* Added enable_eager

* r/force_run_distributed/force_v2_in_keras_compile

* Adding force_v2 tests and FLAGs.

* Rename method to avoid conflict.

* Add cpu force_v2 tests.

* fix lint, wrap line.

* change to force_v2_in_keras_compile

* Update method name.

* Lower mlperf target to 0.736.

This combination does not yet work. Fail early with an explicit message instead of throwing error later on.

* Ncf perf changes 1)exclude metric layer from CTL train step 2)dataset optimization to fix size of the sample_weights, preventing a costly broadcast during loss calculation for multi-gpu case

* reduce iterations from 20 to 12.

* add fp16 dynamic batch accuracy check.

* fix existing lint issue.

* borrowing a tf1.x optimization which converts gradients from sparse to dense for better perf

* cleanup after code review

* XLA FP32 and first test

* More XLA benchmarks FP32.

* Add eager to NCF and refactor resnet.

* fix v2_0 calls and more flag refactor.

* Remove extra flag args.

* 90 epoch default

* add return

* remove xla not used by estimator.

* Remove duplicate run_eagerly.

* fix flag defaults.

* Remove fp16_implementation flag option.

* Remove stop early on mlperf test.

* remove unneeded args.

* load flags from keras mains.

* Add CTL benchmark

* Divide train loss by number of train steps

* increase num epochs to 10

* add benchmark for early stopping with CTL

* remove whitespace

* Fix various lint errors

* Fix logging format

* Add flag to use custom training loop for keras NCF model.

* Add error check to NCF model for custom training loop + tf1.0.

* Add a custom training loop for NCF model with TF2.0

* Fix long line in ncf_keras_main.py

* Remove dataset repeat when using custom training loop.